Yazar "Mashinini, Peter Madindwa" seçeneğine göre listele

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    Enhancing surface quality and tool life in SLM-machined components with Dual-MQL approach
    (Elsevier, 2024) Ross, Nimel Sworna; Mashinini, Peter Madindwa; Mishra, Priyanka; Ananth, M. Belsam Jeba; Mustafa, Sithara Mohamed; Gupta, Munish Kumar; Korkmaz, Mehmet Erdi
    Selective laser melting (SLM) can produce complex metal components with high densities, thereby surpassing the limitations of traditional machining methods. However, achieving accurate dimensions, geometries, and acceptable surface states in parts fabricated through SLM remains a concern as they often fall short compared to traditionally machined components. As a solution, a hybrid additive-subtractive manufacturing (HASM) method was developed to effectively utilize the advantages of both techniques. In this study, SLM-made 316 L stainless steel was machined under distinct cooling conditions to investigate the effects of roughness and tool wear. After a thorough investigation, the dual-MQL strategy was evaluated and compared with dry and MQL cutting strategies. The findings showed that the dual-MQL condition led to a significant reduction in flank wear by 54-56% and 29-34%, respectively, associated with dry and MQL cutting techniques, making it a highly promising key for machining SLM-made steel components. Machine learning techniques are potential tools for prediction and classification capabilities in machining processes. For milling SLM-made 316 L SS, multilayer perceptron (MLP) proved to be the most effective prediction model and for classification MLP and Random forest performed better.
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    A new intelligent approach of surface roughness measurement in sustainable machining of AM-316L stainless steel with deep learning models
    (Elsevier Sci Ltd, 2024) Ross, Nimel Sworna; Mashinini, Peter Madindwa; Shibi, C. Sherin; Gupta, Munish Kumar; Korkmaz, Mehmet Erdi; Krolczyk, Grzegorz M.; Sharma, Vishal S.
    Due to the manufacturing sector ' s digitalization and ability to combine quality measurement and production data, machine learning and deep learning for quality assurance hold enormous potential. In this situation, industries may process data to inform data-driven estimates of product quality, thanks to predictive excellence. This research investigates the machinability of Laser Powder Bed Fusion (LPBF) - 316L stainless steel specimens, focusing on the impact of cutting parameters and cooling conditions (Dry, MQL, CO 2 and CO 2 + MQL) on surface roughness. The research employs advanced data augmentation techniques, incorporating TransGAN and multihead attention (MHA) based Alexnet model for surface imperfection classification. The results highlight the effectiveness of the proposed methodology in accurately classifying surface conditions and underscore the superior performance of the MHA-Alexnet algorithm compared to alternative models (Alexnet and AE-Alexnet). Overall, the study contributes valuable insights into optimizing machining parameters and cooling strategies for enhanced surface finish in additively manufactured alloys.
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    Novel use of cryogenic cooling conditions in improving the machining performance of Al 8011/nano-SiC composites
    (Springer London Ltd, 2023) Ross, Nimel Sworna; Selvin, Belsam Jeba Ananth Manasea; Nagarajan, Srinivasan; Mashinini, Peter Madindwa; Dharmalingam, Satish Kumar; Savio, Akash Paul; Gupta, Munish Kumar
    The inclusion of nanoparticles makes the composite not only stronger but also lighter and highly resistant towards wear among many other positive attributes. However, the high hardness and abrasive characteristics of the composites make machining a formidable task. Hence to surmount these challenges, various coolant conditions have been entailed like dry machining, flood cooling, minimum quantity lubrication (MQL), and cryogenic (cryo) CO2 cooling. This investigation encompasses the influence of diverse coolant techniques during the machining of as casted aluminium with nano silicon carbide (Al/n-SiC) composite. This study further incites the analysis of the machining temperature, surface characteristics, flank wear, and chip morphology under each coolant techniques. The outcomes of this investigation furnish a comprehensive understanding of the impact of distinct coolant environments on the machining performance of Al/n-SiC composite. The cutting temperature under cryo-CO2 was found to be lowered by 41-47%, 15-21%, and 8-12% when compared to the usage of dry, flood, and MQL, respectively. The study unveils that cryo-CO2 cooling developed the lowest machining temperature, followed by MQL, flood cooling, and dry machining. Furthermore, cryo-CO2 cooling and MQL exhibited the best outcome in terms of flank wear and surface characteristics. The verdicts of this investigation suggest the use of cryo-CO2 cooling and MQL makes eloquent improvement in the machining performances of Al/n-SiC composites.
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    Tribology-driven strategies for tool wear reduction and surface integrity enhancement in cryogenic CO2-cooled milling of laser metal deposited Ti64 alloy
    (Elsevier Sci Ltd, 2024) Ross, Nimel Sworna; Mashinini, Peter Madindwa; Ananth, M. Belsam Jeba; Srinivasan, N.; Gupta, Munish Kumar; Korkmaz, Mehmet Erdi
    Additive manufacturing (AM) is chosen for its ability to streamline production processes and design freedom. This reduces material waste, enables rapid prototyping, and facilitates intricate geometries, ultimately offering cost-effective and customizable solutions for manufacturing complex components in diverse industries. Overlapping melting trajectories result in a low-quality surface (Ra=similar to 13.34 mu m) in the laser metal deposition (LMD) of the Ti64 alloy. Therefore, post-processing is often essential for AMed parts for engineering applications. Milling trials were conducted on AMed specimens under four environmental conditions: dry, flood, minimum quantity lubrication (MQL), and cryogenic medium. The machinability was evaluated in terms of the cutting temperature, machined surface roughness, tool wear, chip morphology, and microhardness. The flank wear under cryogenic CO2 condition is 52.78-54.29 % lower than dry condition, 33.86-36.24 % lower than flood cutting, and 23.64-26.86 % lower than MQL. The outcomes show that cryogenic cooling augments the tool life and the surface integrity of milling LMD parts. Moreover, the hardness under cryogenic CO2 was higher, indicating dimensional stability and maintenance of shape integrity under applied loads.